Recently, many annotation efficient learning-based works have been employed to reduce medical image annotation costs [1]}, [2]}, [3]}, [4]}. However, most of them are semi-supervised learning-based methods, which still need to annotate part of the dataset carefully. Weakly supervised learning just requires very few sparse annotations to learn and achieve promising results [5]}, [6]}. Fig. REF  shows an example of different types of medical image annotation. It shows that the sparse annotation just requires very few label costs, less than 4% compared with dense annotation. In this work, we evaluate several weakly-supervised methods on the abdominal multi-organ segmentation task for the first time and further propose a new method to boost the results.
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